AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 347 · 40 questions

AI tip (Fair data, fairer AI): Bias usually comes from the training data: if a group is missing or shown in only one way, the model copies that. One-sided data: one age group, only ripe mangoes, only adult voices, only neat handwriting. Balanced data: many ages, colours, voices, regions and styles. Reduce bias: add varied examples, check who is missing, test on many groups, fix wrong labels and listen to complaints.

1. 1.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its colour (c) its training data (d) the weather
2. 2.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
3. 3.Which of these adds to bias? (a) add varied examples (b) ignore complaints (c) ask different people (d) balance the data
4. 4.🔍 Which is the odd one out? (a) test on many groups (b) fix wrong labels (c) ask different people (d) ignore complaints
5. 5.Which of these helps reduce bias? (a) ignore complaints (b) add more of the same (c) add varied examples (d) use one group only
6. 6.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
7. 7.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
8. 8.True or false: “balance the data” helps reduce bias. (a) True (b) False
9. 9.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) a broken screen (c) bias from data (d) a fair model
10. 10.True or false: “check who is missing” helps reduce bias. (a) False (b) True
11. 11.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
12. 12.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
13. 13.Which of these adds to bias? (a) test on many groups (b) add varied examples (c) check who is missing (d) add more of the same
14. 14.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
15. 15.🛠️ What helps make an AI system fairer? (a) one group only (b) more varied data (c) fewer tests (d) ignoring mistakes
16. 16.True or false: a voice app trained only on adult voices is one-sided data (likely to make a biased model). (a) False (b) True
17. 17.True or false: “fix wrong labels” adds to bias. (a) False (b) True
18. 18.🗣️ A voice app understands adults but not children. What is the likely reason? (a) few child voices in data (b) it was a rainy day (c) the phone was new (d) children speak too fast
19. 19.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True
20. 20.True or false: a leaf app trained only on summer leaves is one-sided data (likely to make a biased model). (a) False (b) True
21. 21.🔍 Which is the odd one out? (a) add varied examples (b) ask different people (c) fix wrong labels (d) add more of the same
22. 22.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
23. 23.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
24. 24.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) True (b) False
25. 25.Which of these helps reduce bias? (a) copy old unfair choices (b) ignore complaints (c) test on many groups (d) use one group only
26. 26.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
27. 27.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) True (b) False
28. 28.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
29. 29.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
30. 30.Which of these adds to bias? (a) check who is missing (b) copy old unfair choices (c) test on many groups (d) add varied examples
31. 31.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
32. 32.Which of these helps reduce bias? (a) ignore complaints (b) skip testing (c) check who is missing (d) add more of the same
33. 33.Which of these helps reduce bias? (a) skip testing (b) ignore complaints (c) balance the data (d) test on one group only
34. 34.True or false: photos of people of many ages and skin tones is one-sided data (likely to make a biased model). (a) True (b) False
35. 35.True or false: “add varied examples” helps reduce bias. (a) False (b) True
36. 36.True or false: “fix wrong labels” helps reduce bias. (a) True (b) False
37. 37.True or false: “test on many groups” helps reduce bias. (a) False (b) True
38. 38.Which of these helps reduce bias? (a) use one group only (b) copy old unfair choices (c) fix wrong labels (d) ignore complaints
39. 39.True or false: “balance the data” adds to bias. (a) True (b) False
40. 40.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias

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